How Automation Improves Fraud Detection Strategies

Here's how Stripe detects frauds with a 99.9% accuracy in 100 milliseconds (that too by checking over 1000 parameters for one transaction) Fraud detection in online payments isn’t just about stopping bad transactions it’s about doing it fast, at scale, and without blocking legitimate users. Stripe’s fraud prevention system, Radar, evaluates 1,000+ signals within 100 milliseconds to make decisions. Here’s how it works and why it’s so effective: 1. ML Models That Learn and Scale Stripe started with simple ML models (logistic regression) but quickly scaled to hybrid architectures combining: –XGBoost for memorization (catching known patterns). –Deep Neural Networks (DNNs) for generalization (handling unseen patterns). –Key Problem: XGBoost couldn’t scale or integrate modern ML techniques like transfer learning and embeddings. –The Solution: Stripe moved to a multi-branch DNN-only architecture inspired by ResNeXt. This setup allowed it to memorize patterns while staying scalable. It reduced training times by 85%, enabling multiple experiments in a single day instead of overnight runs. 2. Learning From Real Fraud Patterns Radar doesn’t just rely on static rules, it learns from data across Stripe’s network. –Engineers analyze fraud attacks in detail, e.g., patterns of disposable emails or repeated card testing. –Features like IP clustering and velocity checks were added to detect suspicious activity. –Fraud insights are shared across the network, so lessons learned from one business protect others automatically. Example: Analyzing IP patterns helped detect high-volume attacks where fraudsters used multiple stolen cards from the same source. 3. Scaling With More Data, Not Just Smarter Models Stripe realized that more training data could unlock better performance, similar to modern LLMs like GPT models. It tested scaling datasets by 10x and 100x. Result? Performance kept improving, confirming that larger datasets and faster training cycles work better than complex rules alone. Key Insight: Bigger datasets help uncover rare fraud cases, even if they occur in only 0.1% of transactions. 4. Explaining Fraud Decisions Clearly Fraud systems often act like black boxes, leaving businesses guessing why a payment failed. Stripe built Risk Insights to provide clear explanations: –Shows features contributing to fraud scores like mismatched billing and shipping addresses. –Displays maps and transaction histories for visual context. –Enables custom rules to fine-tune fraud checks for specific business needs. Result: Businesses trust Radar’s decisions because they can see why a payment was flagged. 5. Constant Adaptation to Stay Ahead Fraud patterns evolve, so Stripe built Radar to adapt in real time: Uses transfer learning and multi-task learning to generalize better. Incorporates insights from the dark web and emerging fraud tactics. Continuously retrains models without disrupting performance.

Just had a call with a customer whose risk analysts are firmly stuck in the pre-AI world. Here's what they're doing manually that will be automated: (Disclaimer: I don't blame this team at all, and there are so many like them making the jump to AI workflows. We're here to help!) Their current (manual) merchant verification process: 1. Manually searching business names across multiple sources 🔍 2. Cross-checking Secretary of State registrations 📑 3. Comparing website domain creation dates with "in business since" claims 📅 4. Reviewing Google/Yelp business status and ratings ⭐ 5. Scanning for adverse media mentions 📰 6. Checking physical location via Google Maps 🏢 7. Verifying social media presence (Instagram/YouTube) 📱 8. Looking for suspicious website elements (stock images, template text) 🚩 9. Verifying the payout bank account with voided checks 🏦 10. Calculating potential credit exposure for risk assessment 💰 Every analyst does this, and I don't blame them. The problem is, it's time-consuming, inconsistent across analysts and teams, and doesn't scale 👎 What excites me is how AI agents 🧠 can transform this workflow: - Automated data collection: Connect to multiple sources simultaneously to gather all relevant data in seconds ⚡ - Pattern recognition: Flag discrepancies that matter (like a business claiming 20 years of history with a 6-month-old domain) 🧩 - Contextual intelligence: Understand industry norms (like towing companies typically having lower ratings) 🔄 - Risk summarization: Provide the "net net" with key findings and specific risk factors, not raw data dumps 📊 - Guided recommendations: "Pause payouts," "Request additional documentation," or "Approve with monitoring" based on risk patterns and the company's risk appetite 📋 - Continuous learning: Improve detection by incorporating feedback from confirmed fraud cases 📈 Transitions like this are difficult once. The outcome is a senior analyst team member for everyone on your risk team that never gets tired, and always delivers insights. Leaving the manual processes behind forever. Trust me, it's worth it 🚀

Mastercard's recent integration of GenAI into its Fraud platform, Decision Intelligence Pro, has caught my attention. The results are impressive and shows the potential of “GenAI in Advanced Business Applications”. As someone who follows AI advancements in Fraud across the FSI industry, this news is genuinely exciting. The transformative capabilities of GenAI in fortifying consumer protection against evolving financial fraud threats showcase the potential impact of this integration for improving the robustness of AI models detecting fraud. The financial services sector faces an escalating threat from fraud, including evolving cyber threats that pose significant challenges. A recent study by Juniper Research forecasts global cumulative merchant losses exceeding $343 billion due to online payment fraud between 2023 and 2027. Mastercard's groundbreaking approach to fraud prevention with GenAI integrated Decision Intelligence Pro is revolutionary. - Processing a staggering 143 billion transactions annually, DI Pro conducts real-time scrutiny of an unprecedented one trillion data points, enabling rapid fraud detection in just 50 milliseconds. - This innovation results in an average 20% increase in fraud detection rates, reaching up to 300% improvement in specific instances. As we consider strategic imperatives for AI advancement in fraud, this news suggests what future AI models must prioritize: - Rapid analysis of vast datasets in real-time, maintain agility to counter emerging fraudulent tactics effectively, and assess relationships between entities in a transaction. - By adopting a proactive approach, AI systems should anticipate and deflect potential fraudulent events, evolving and learning from emerging threats to bolster security. - Addressing the challenge of false positives by evolving AI models capable of accurately distinguishing legitimate transactions from fraudulent ones is vital to enhancing overall security accuracy. - Committing to continuous innovation embracing AI is essential to maintaining a secure and trustworthy financial ecosystem. #artificialintelligence #technology #innovation

The real breakthrough of AI in auditing is not automation - it’s autonomy. For years, AI in audit has been used for task-based automation: scanning reports, reconciling transactions, and highlighting discrepancies. But Agentic AI takes it a step further. It doesn’t just flag issues - it investigates them, cross-referencing internal data with external risk factors to assess intent and likelihood of fraud. Today’s AI: Identifies anomalies, flags suspicious transactions, and requires human oversight. Tomorrow’s AI: Assesses fraud probability, suggests corrective actions, and autonomously detects new fraud schemes before they surface. Agentic AI doesn’t just say, “This transaction looks off,” but rather: “This pattern suggests an employee is routing funds through a third-party shell company. Here’s supporting evidence, historical comparisons, and recommended next steps.” This is the shift from audit assistant to AI-driven fraud investigator. The future isn’t just AI-powered auditing - it’s AI-led fraud prevention. The financial world is changing. The companies that build AI-first risk management strategies will be the ones that stay ahead. Are we ready to let AI take a more active role in financial integrity, or are we still too reliant on human oversight?

𝐇𝐨𝐰 𝐀𝐈 𝐦𝐢𝐭𝐢𝐠𝐚𝐭𝐞𝐬 𝐟𝐫𝐚𝐮𝐝 𝐢𝐧 𝐀𝐜𝐜𝐨𝐮𝐧𝐭-𝐭𝐨-𝐀𝐜𝐜𝐨𝐮𝐧𝐭 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 by Visa👇 — 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐧 𝐀2𝐀 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬: ► Account-to-Account (A2A) payments are rapidly growing, with a forecasted 161% growth between 2024 and 2028. ► The fundamental characteristics of Real-Time Payments (RTP), such as speed, 24/7 availability, irrevocability, and lack of network visibility, contribute to the increasing fraud risks. ► Fraud is evolving with the growth of A2A payments, making it crucial for financial institutions to implement real-time fraud prevention strategies. — 𝐖𝐡𝐲 𝐢𝐬 𝐀𝐈 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐅𝐫𝐚𝐮𝐝 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧? ► 𝐒𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: AI enables real-time fraud detection and prevention, essential for instant payment transactions that are completed within 10 seconds. ► 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝐑𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧: AI can recognize patterns and detect irregularities, linked to mule accounts or changed geolocation. ► 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: AI models adjust to new fraud trends in real-time, unlike traditional rules-based systems that require post-loss analysis. ► 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐅𝐚𝐥𝐬𝐞 𝐏𝐨𝐬𝐢𝐭𝐢𝐯𝐞𝐬: AI-enhanced systems provide more accurate fraud detection, reducing the need for manual reviews and minimizing false positives. ► 𝐍𝐞𝐭𝐰𝐨𝐫𝐤-𝐋𝐞𝐯𝐞𝐥 𝐕𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: AI leverages a multi-financial institution (FI) view, enabling a comprehensive view of fraud across payment networks, which is crucial for detecting cross-network fraud schemes. — 𝐑𝐮𝐥𝐞𝐬-𝐁𝐚𝐬𝐞𝐝 vs. 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: 𝐑𝐮𝐥𝐞𝐬-𝐁𝐚𝐬𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦: 1️⃣ Transaction Initiated 2️⃣ Massive Volume of Transactions: High volume of transactions are flagged for manual review due to basic rule triggers. 3️⃣ Manual Review: Transactions are manually reviewed, leading to delays and operational inefficiencies. 4️⃣ Transaction Assessed: Risk is evaluated based on pre-set rules. 5️⃣ Transaction Authorized: If no rule is violated, the payment is authorized. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: High false positives, time-consuming manual reviews, and delays in payment processing. 🆚 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦: 1️⃣ Transaction Initiated 2️⃣ Curated Volume of Transactions: AI intelligently filters transactions, reducing the volume that requires review. 3️⃣ AI-Assisted Review: Transactions are reviewed with AI input, providing real-time risk assessment. 4️⃣ Data & Model Assessment: AI evaluates transactions using data patterns and predictive models. 5️⃣ Transaction Authorized: If deemed low-risk, the payment is instantly authorized. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: Reduced false positives, real-time risk assessment, operational efficiency, and improved customer experience. — Source: Visa — ► Sign up to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬 ☕: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... and Marcel van Oost

Fraud detection - it's a big concern for every bank, right? We’ve all seen the headlines: millions lost in fraudulent transactions, and customer trust hanging in the balance. But what if you could stop fraud before it even happens? That’s exactly what we’re doing with Azure Databricks to fight real-time fraud. Here’s how we’re making it happen: - Stream the data in You’ve got loads of transactions happening every second. We pull them in via Azure Event Hubs and stream all that live data. - Clean it up Azure Databricks takes over here filtering, cleaning, and analyzing everything in real time. We’re using machine learning models to flag anything that looks off or unusual. - Train the models Here’s where Azure Machine Learning comes in. We’re feeding historical data into the models to teach them what fraud looks like. Over time, they get better and better at spotting it. - Store and analyze We’re moving the refined data to Azure Synapse Analytics. That’s where you can really dig in and analyze what’s happening. - Dashboards, of course All the flagged transactions show up in Power BI dashboards so the fraud team can see what’s going on in real-time and act fast. Why does all this matter? Because in real-time fraud detection, every second counts. Stopping fraud early doesn’t just save millions- it builds customer trust. P.S.: What’s your go-to strategy for fraud prevention these days? #AzureDatabricks #Banking #FraudDetection #Azure #DataScience #simform 

I wrote a piece recently about how I think of AI: "Adapt Now or Fall Behind". The core of it is that fraudsters are leveraging AI really well already. This is leading to an unprecedented increase in scams, making it difficult for most to differentiate between what is real or not. At Unit21, we’ve already seen data showing that 40% of transactions that were blocked were due to scams 🤯 We need to lean into AI to fight back. The way businesses use AI will determine whether we win this war. AI has countless applications, from asking ChatGPT about data trends to deploying AI agents for Level 1 fraud/AML reviews or entity research. At Unit21, we focused on the risk-reward question: Where are the lowest risk and highest reward? We realized that 80% of the work in a fraud or AML alert is just gathering information, not decision-making. So, we prioritized optimizing that process. It’s explainable, keeps humans in the loop for the final decision, and significantly reduces manual work by 80% with minimal risk. The key is to start with the lowest-risk, highest-reward tasks and scale up over time. As an industry, AI adoption must also align with regulatory approval. The goal isn’t to have AI make the final decision—that’s unacceptable. We have to leverage AI to automate processes with full explainability. If your current technology doesn’t offer AI-driven solutions, it’s time to make an exit plan. Thrilled to be leading the charge on AI. We need to lean in. We must WIN this fight against fraud 💪 #ai #winning #fraudfighters #complianceheroes

One of AI's superpowers is its ability to comb through vast amounts of data and spot patterns -- all of this at lightning-fast speed. That's why the US government is turning to AI to fight financial crime. Treasury officials tell CNN this has been a gamechanger -- especially when it comes to combating check fraud. For the first time, the Treasury Department says it's using AI-powered fraud detection methods, recovering $375 million in fiscal 2023 alone & helping to lead to multiple arrests. “Once you train a model, the speed you can catch things is in milliseconds. It’s incredible,” Amiram Shachar from Upwind Security told me. More with Richard Quest on CNN Max and CNNi

Excited to share insights on Building AI Apps on a Solid Data Foundation! Ensuring reliable, accessible, and efficiently processed data is key for AI applications. Technologies like Debezium, Apache Iceberg, Apache Kafka, Spark, and Trino are crucial in this process. 🔹Debezium and Change Data Capture: - Debezium captures database changes in real-time to enable quick responses. - Change Data Capture ensures up-to-date data in data lakes and warehouses. 🔹Apache Iceberg: - High-performance table format for large datasets with features like partitioning and time-travel queries. - Integrates with Debezium for real-time data ingestion. 🔹Apache Kafka and Kafka Connect: - Kafka builds real-time data pipelines and partners with Debezium. - Kafka Connect links Kafka with external systems for data streaming. 🔹Spark and Trino: - Powerful data processing engines that collaborate with Iceberg tables for analytics. - Spark handles large datasets, while Trino allows fast analytics across sources. Exploring a Fraud Detection Use Case: - Combine technologies to create a robust data pipeline for fraud detection. - Debezium captures changes, Kafka Connect writes to Iceberg tables, Spark processes data, and Trino handles queries. - Processed data trains AI models for fraud detection using real-time and historical data in Iceberg. #AI #DataFoundation #FraudDetection #Apache #Technology #DataProcessing